Multi-dimensional wrapper to compute the multi-dimensional matrix profile and multi-dimensional matrix profile index for a given window within the times series or sequence that is denoted by the `start` index. Essentially, this is a convenience wrapper around `_multi_mass`. This is
(
start,
T_A,
T_B,
m,
excl_zone,
M_T,
Σ_T,
μ_Q,
σ_Q,
T_subseq_isconstant,
Q_subseq_isconstant,
include=None,
discords=False,
)
| 656 | |
| 657 | |
| 658 | def _get_first_mstump_profile( |
| 659 | start, |
| 660 | T_A, |
| 661 | T_B, |
| 662 | m, |
| 663 | excl_zone, |
| 664 | M_T, |
| 665 | Σ_T, |
| 666 | μ_Q, |
| 667 | σ_Q, |
| 668 | T_subseq_isconstant, |
| 669 | Q_subseq_isconstant, |
| 670 | include=None, |
| 671 | discords=False, |
| 672 | ): |
| 673 | """ |
| 674 | Multi-dimensional wrapper to compute the multi-dimensional matrix profile |
| 675 | and multi-dimensional matrix profile index for a given window within the |
| 676 | times series or sequence that is denoted by the `start` index. |
| 677 | Essentially, this is a convenience wrapper around `_multi_mass`. This is a |
| 678 | convenience wrapper for the `_multi_distance_profile` function but does not |
| 679 | return the multi-dimensional matrix profile subspace. |
| 680 | |
| 681 | Parameters |
| 682 | ---------- |
| 683 | start : int |
| 684 | The window index to calculate the first multi-dimensional matrix profile, |
| 685 | multi-dimensional matrix profile indices, and multi-dimensional subspace. |
| 686 | |
| 687 | T_A : numpy.ndarray |
| 688 | The time series or sequence for which the multi-dimensional matrix profile, |
| 689 | multi-dimensional matrix profile indices, and multi-dimensional subspace will be |
| 690 | returned |
| 691 | |
| 692 | T_B : numpy.ndarray |
| 693 | The time series or sequence that contains your query subsequences |
| 694 | |
| 695 | m : int |
| 696 | Window size |
| 697 | |
| 698 | excl_zone : int |
| 699 | The half width for the exclusion zone relative to the `start`. |
| 700 | |
| 701 | M_T : numpy.ndarray |
| 702 | Sliding mean for `T_A` |
| 703 | |
| 704 | Σ_T : numpy.ndarray |
| 705 | Sliding standard deviation for `T_A` |
| 706 | |
| 707 | μ_Q : numpy.ndarray |
| 708 | Sliding mean for `T_B` |
| 709 | |
| 710 | σ_Q : numpy.ndarray |
| 711 | Sliding standard deviation for `T_B` |
| 712 | |
| 713 | T_subseq_isconstant : numpy.ndarray |
| 714 | A boolean array that indicates whether a subsequence in `T_A` is constant (True) |
| 715 |